This model is a fine-tuned version of google/vit-base-patch16-224 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.1924 - Accuracy: 0.9445 This model is a fine-tuned version of a pre-trained Vision Transformer (`google/vit-base-patch16-224`) for image classification on the Oxford-IIIT Pet Dataset. It uses transfer learning to adapt a generic vision model to identify 37 different cat and dog breeds. The model head is adjusted to output the number of classes in the dataset, and it is trained end-to-end using standard classification loss. **Intended Uses:** - Educational demos on transfer learning and fine-tuning vision models. - Pet breed classification in structured datasets similar to Oxford Pets. - Comparative analysis with zero-shot models like CLIP.
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View details ↗**BLT (Byte Latent Transformer)** is a tokenizer-free transformer architecture that operates directly on raw byte sequences. Instead of processing text token by token, BLT dynamically groups bytes into **entropy-based patches**, enabling more efficient and scalable processing for byte-level tasks. Key components: - **Local Encoder → Latent Transformer → Local Decoder** architecture. - **Entropy-based patcher (BltPatcher)**: scans byte streams and creates patches when entropy thresholds are met. - **Hash n-gram embeddings**: maintain contextual information over neighboring bytes. BLT achieves competitive performance compared to traditional token-based transformers, supporting multilingual, noisy, or mixed-script input.
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View details ↗MOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across **50+** languages, single-pass inference on audio recordings up to **90 minutes** long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as `[S01]`, `[S02]`, and beyond.
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